BOL: Related items

kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data

Jit — Mon, 07 Jan 2019 10:35:14 -0600

kallisto is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools. kallisto is described in detail in:

Nicolas L Bray, Harold Pimentel, Páll Melsted and Lior Pachter, Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525–527 (2016), doi:10.1038/nbt.3519

Address of the bookmark: https://pachterlab.github.io/kallisto/about

heatmaply: popular graphical method for visualizing high-dimensional data

Neel — Sat, 11 Jan 2020 07:34:14 -0600

This work is based on ggplot2 and plotly.js engine. It produces similar heatmaps as d3heatmap, with the advantage of speed (plotly.js is able to handle larger size matrix), and the ability to zoom from the dendrogram.

heatmaply also provides an interface based around the plotly R package. This interface can be used by choosing plot_method = "plotly" instead of the default plot_method = "ggplot". This interface can provide smaller objects and faster rendering to disk in many cases and provides otherwise almost identical features.

Documentation for this package is also available as a pkgdown site: http://talgalili.github.io/heatmaply/

Address of the bookmark: http://talgalili.github.io/heatmaply/articles/heatmaply.html

Free Genomics data !

BioStar — Fri, 07 Feb 2020 14:08:31 -0600

The specimens were collected by the Oxford Wytham Woods and Edinburgh Lohse lab teams. DNA extraction and sequencing was carried out by the Sanger Institute Scientific Operations teams. Assemblies were carried out by the Tree of Life team (Shane McCarthy) and colleagues in Pacific Biosciences (Jonas Korlach).

https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Address of the bookmark: https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Submit your SARS-CoV-2 sequence data to GenBank

Neel — Thu, 09 Apr 2020 18:28:25 -0500

Submit your SARS-CoV-2 sequence data to GenBank and SRA with our new submission landing page. Submission is simple and streamlined *and* there’s a rapid turnaround. https://submit.ncbi.nlm.nih.gov/sarscov2/

Quickly and easily add your SARS-CoV-2 sequence data to the growing public archive with new, special features and support from NCBI. new SARS-CoV-2 sequence submission landing page will help you get started. GenBank submissions are accessioned and released in approximately 1-2 working days, and Sequence Read Archive (SRA) submissions typically processed and released within hours. Submission is simple!

More information is available on NCBI Insights. https://ncbiinsights.ncbi.nlm.nih.gov/2020/04/09/sars-cov2-data-streamlined-submission-rapid-turnaround/

GraphUnzip: Phases an assembly graph using Hi-C data and/or long reads.

Jit — Fri, 05 Feb 2021 21:22:24 -0600

GraphUnzip, a fast, memory-efficient and accurate tool to unzip assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences in the assembly graph that already had a potential link based on overlaps, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on a simulated diploid Escherichia coli genome, and on two real datasets for the genomes of the rotifer Adineta vaga and the potato Solanum tuberosum. In all cases, GraphUnzip yielded highly continuous phased assemblies.

https://www.biorxiv.org/content/biorxiv/early/2021/02/01/2021.01.29.428779.full.pdf

Address of the bookmark: https://github.com/nadegeguiglielmoni/GraphUnzip

Corona Virus Genome and Data Download !

Abhi — Sun, 12 Dec 2021 23:34:54 -0600

Genes and its related metadata could be found on https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/

Orange: Data mining

BioStar — Mon, 13 Mar 2023 12:42:29 -0500

Open source machine learning and data visualization.

Build data analysis workflows visually, with a large, diverse toolbox.

Address of the bookmark: https://orangedatamining.com/

BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data

Neel — Wed, 13 Aug 2025 02:31:28 -0500

2,438,386 bacterial genomes at your fingertips consistently processed & characterized, enriched with metadata, accessible via a flexible search engine.

BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data. It integrates enriched genomic information with metadata accessible via a flexible search-engine.

Key features

Assembly statistics: ensure data quality with genome-based key metrics
Taxonomic classification: robust, purely genome-based classifications (GTDB)
MLST: subtyping for deeper insights into genetic variation
Annotation: comprehensive & taxonomy-independent (Bakta)
Metadata: full original submission records

Address of the bookmark: https://bakrep.computational.bio/

GA4GH Data Working Group

Jitendra Prajapati — Sun, 20 Mar 2016 23:13:07 -0500

GA4GH Data Working Group

Led by David Haussler (UCSC) and Richard Durbin (Sanger Institute), the Data Working Group (DWG) of the Global Alliance brings together the leading Genome Institutes and Centers with IT industry leaders to create global standards and tools for the secure, privacy respecting and interoperable sharing of Genomic data.

More at http://ga4gh.org/#/

Address of the bookmark: http://ga4gh.org/#/

NCBI to assist in Virus Hunting Data Science Hackathon

BioStar — Thu, 15 Nov 2018 12:55:01 -0600

NCBI Hackathon are pleased to announce the second installment of the SoCal Bioinformatics Hackathon. From January 9-11, 2019, the NCBI will help run a bioinformatics hackathon in Southern California hosted by the Computational Sciences Research Center at San Diego State University!

NCBI Hackathon specifically looking for folks who have experience in computational virus hunting or adjacent fields to identify known, taxonomically-definable and novel viruses from a few hundred thousand metagenomic datasets that we’ll put on cloud infrastructure. This event is for researchers, including students and postdocs, who are already engaged in the use of bioinformatics data or in the development of pipelines for virological analyses from high-throughput experiments. If this describes you, please apply! The event is open to anyone selected for the hackathon and willing to travel to SDSU (see below).

https://ncbiinsights.ncbi.nlm.nih.gov/2018/11/09/ncbi-sdsu-virus-hunting-data-science-hackathon-january-2019/